シャドーイング練習: Databricks Interview Questions Part 1 - 動画で英語スピーキングを学ぶ

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hello and welcome to everybody on cloud fitness

so this video is a very on demand video where i

am starting a play series on the data factory interview questions

so a lot of you have sent in emails to me right

you know to make videos on azure data factory

and databricks interview questions so

that is why i have already created a video of a playlist for azure data factory

and this one is for the databricks so let's move ahead

and see what are the questions

that you know we we can start off from this particular video right

so the very first basic question that I have is what is Databricks

so this question might be asked in the interviews just to understand how much you know Databricks

so you should be able to answer it really well right

so when we talk about is your Databricks it is an Apache Spark based environment

that is what the like the very first thing is

that you should know that it is an Apache Spark based environment it is a collaborative environment where when I say collaborative,

it essentially means that you have your data engineers, you have your data analysts,

you have your data science people coming in all together at the one place and working on the data, right?

It is an Apache Spark based big data analytics service.

When I say

Apache Spark, whatever code that you write in Databricks, that is basically run on Spark, right?

It is a Spark code that you write.

Now, apart from that, it is a big data analytics service it's not just the structured data

that you can process it's you can process semi -structured

and even unstructured data as well in the databricks now the second thing is um you know that

it supports multiple language

so it is not very much language bound you can work on scala

you can work on r you can work on python you can work on sql as well

so these are the things

that you should definitely know about databricks as well as it

can you know easily integrate with your other Azure services like

your you know your active directory your keywords your storage accounts

if you have data in your storage accounts you can directly go ahead link it

and use that data right so it is a very essential

and useful SaaS offering so in fact let us go to the second question

that is also very commonly asked because

when you are writing Databricks in your resume what usually happens

is a person who has worked on Databricks essentially would know about Data Factory as well

So this is a very common question that what is the difference between Databricks and DataFactory?

Because whatever code you write in Databricks, let's say you're trying to do a filter,

group buy or anything, that can essentially be done in DataFactory, right?

Or vice versa.

So why do you need Databricks when it can be done in DataFactory?

So you should be very clear.

It is essentially a very, you know, both are actually different offerings.

The very first difference is the purpose, right? the data bricks has a different purpose

and data factory has a different purpose data factory is essentially

for orchestration for the data flow for the data transformation for the code free data transformation

and orchestration but

when you talk about data bricks i have already explained

it is an collaborative platform where multiple people can come in together data science team can come

and they can train their models they can train their models in data bricks

when you talk about data engineers they can write a code solution

so code solution is actually not bad

because you have a lot of flexibility uh you know with

when you write your own code which you are

which you don't get with the data factory right so

that is essentially one of the reason that most of the organization

when there is any kind of you know data engineering task

or data engineering transformations

that have to happen on the code

they prefer using data bricks uh rather than data factory data factory is mostly

you know most of the organization they use for uh data movement

and orchestration purpose so this is the very key and major difference

when you talk about data breaks and the data factory

and of course

when you talk about data processing as well right in data uh breaks

you can process not just your structured semi -structured

or even your you know your batch data you can easily uh

you know use your streaming data as well though in data factory also

you have an option uh to use streaming data

but live streaming is possible only in databricks as of now right

so even when you talk about data processing databricks has an advantage

but yeah these are the two uh major things apart from

that there are cosmetic things that you can talk about like you can say

that data factory uh you know is a code free etl solution

and those kind of things but these two are the major major differences

that you should definitely cover

and then a very you know common question is describe spark architecture

so if you're working on databricks you should know spark architecture right

so i have already in fact explained about the spark architecture in one of my videos let me check that out

So if you see this runtime architecture of Spark Databricks, I have already explained, you know, in detail regarding the architecture of Spark.

If you want to know details on this, you can go ahead and you can, you know, just watch out this video as well.

In this, I have, you know, explained everything about the runtime architecture of Spark, how everything works and how you're.

In fact, I created this diagram to show, you know, the flow of the basically flow of,

you know, whenever you write a query, what is the flow that happens in your data breaks?

So going back to the PPT, if you see over here, right, when I talk about a Spark architecture,

what essentially happens is you write a code.

I'm actually trying to explain this in very short

if you want to have a very detailed explanation you can watch

that video so

when you talk about spark architecture you write your code whenever

you write your code you write it through spark context you uh you know you write it on spark context

and with the help of driver program driver program actually

or uh you know you you write it through spark context

you know whenever you must be writing code in data breaks you you would write

that spark .rd right sc .parallize so SE is basically spark context right

so you create a driver program

and then behind the scenes what happens is spark driver submits the job submits your code

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you know submits your code to dark scheduler so

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when I say dark scheduler dark scheduler is nothing

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but directed acyclic graph scheduler

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so what it what it does is it essentially takes your code what you have written

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and it tries to create a logical and physical plan out of it

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when i say logical and physical plan out of it it tries to optimize your code

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and tries to create a plan out of it on how it will execute your code

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now once it has created your physical and the logical plan then what happens it it says the

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you know it breaks your code in form of tasks

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and your stages right it breaks your code in form of stages

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and tasks and these tasks are actually sent to your executors

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or to your worker nodes for the execution purpose right

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and then from there executors actually write your data to whichever location you have specified

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and in between cluster manager actually manages your you know all the worker nodes

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and the executors right

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so this is a you know very uh short introduction of how spark architectures behave

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you have your code you write it through you know spark context

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you have your driver program you send your code uh

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you know then you have your cluster manager cluster manager tries to you know make sure

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that you have worker nodes ready

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and like it manages your worker nodes basically worker nodes are nothing

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but a set of machines where your actual code will execute

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now your code is there with the help of spark driver it is sent to

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that scheduler which creates logical

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and physical plan out of it once it is done

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it actually you know segregates your code in form of stages and tasks

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which are supposed to be executed on the executors

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then all those tasks through the with the help of task

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scheduler are sent to your executor nodes for the execution purpose

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so this is basically a very short introduction of spark architecture

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and then coming on to the next question is what is the maximum size of worker node that you have used

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Now, this is a very, very, very important question.

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In fact, the most important one, I would say.

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Now, this essentially shows everything, you know, about how much you have worked on Databricks and how much you know about Spark.

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Essentially, this question answers everything.

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So, when I ask or when anybody asks you what is the maximum size of worker node you have used,

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it shows how much data you have been using it shows

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you know how you how good you are in configuring the cluster

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it shows how much you understand the spark architecture right so whenever this question is asked you should not

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exceed the worker node configuration more than 64 gb that is something

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that you should keep in your mind what is the maximum size of worker node

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you have used essentially it depends on your project as well

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but most of the project they do not need more than 64 gb

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or worker nodes right because what happens is

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if you increase the size of your worker nodes more than 64 gb there is a garbage garbage collection overhead

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which reduces your performance and mostly you will not need

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that right you will not need more than 64 gb after

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this you can you know go ahead increase the number of worker nodes

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or you know you can increase the number of cores you have

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and there are a lot other optimizations to be done

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but 64 is a cross checkpoint like more than 64 gb is actually not required

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and in case if you want to know more more about this why in more than 64

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and all for that also i've created you know, one video on how to select worker and driver type in Databricks.

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So if you go through this particular video, you will understand end -to -end how to choose a cluster, basically.

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How you can go ahead, choose a cluster, and what cluster configuration best suits for each workload.

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But yeah, a straightaway answer is 64 GB.

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If you want answer, detailed answer of why, you can go ahead and check the video out.

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And then you have the next question is, how do you choose cluster configuration?

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So when I say how do you choose cluster configuration, again, this is a very important question and it has,

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you know, you can speak about it for almost half an hour.

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But yeah, again, for choosing the cluster configuration,

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you should understand that whenever you are trying to do any normal ETL or ELD work,

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in that case, you should go for memory optimized clusters right after

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that whenever you have some development tasks that you need to do

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and quickly check it out in

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that case always go for general purpose clusters then the third thing is

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if you have a strong ETL task or ELT task

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which requires a lot of shuffle it has a lot of joins in

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that case always go for storage optimized cluster

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so these things you should always remember these are the key points actually in fact i can actually go

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and show you so whenever you are in the databricks portal

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and you try to create a cluster

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so let me try to create a cluster and in fact show you

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if you go to the worker type

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or the driver type you can actually see there is general purpose

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there is general purpose hdd hdd you should never choose never choose an hdd option

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then you have this memory optimize over here and then after you have storage optimized right

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So these things you should always keep in mind that before attending any interview for data breaks.

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So this really makes sense that for any normal EGL workload, you should always use memory optimized,

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storage optimized for a shuffle intensive workload because it has a disk attached to it.

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So it can easily speed up your process of any shuffle that is required.

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That is why you should go for a storage optimized once in case of shuffle intensive work.

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So, this is pretty much that I wanted to cover in this particular video.

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And thank you so much for being till here.

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Do let me know in the comment section if you have any issues, any doubts.

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And do remember to like, subscribe and share this particular channel.

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Thank you so much.

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単語	発音	意味
spark 名詞	/ˈspɑɹk/	火花, 火の粉
worker 名詞	/ˈwɝ.kɚ/	労働者, 勤労者
node 名詞	/noʊd/	結節
architecture 名詞	/ˈɑː.kɪˌtɛk.t͡ʃə/	建築, 建築学
configuration 名詞	/kənˌfɪɡ.əˈɹeɪ̯.ʃən/	構成, 配置
brick 名詞	/ˈbɹɪk/	煉瓦
submit 動詞	/səbˈmɪt/	提出する
transformation 名詞	/ˌtɹæns.fɔɹˈmeɪ.ʃən/	変形, 変換
execute 動詞	/ˈɛksɪˌkjuːt/	処刑する, 消す
execution 名詞	/ˌɛk.sɪˈkjuː.ʃən/	実行, 執行
garbage 名詞	/ˈɡɑɹ.bɪd͡ʒ/	ごみ, 廃物
analytics 名詞	/ˌæn.əˈlɪt.ɪks/	分析論, 分析学
cloud 名詞	/ˈklaʊ̯d/	雲, 雲霞
select 動詞	/sɪˈlɛkt/	選ぶ
explanation 名詞	/ˌɛkspləˈneɪʃən/	説明

動画に出てくる句動詞

単語	発音	意味
go ahead 動詞	/ˌɡoʊ əˈhɛd/	どうぞお先に
speed up 動詞	/spiːdˈʌp/	スピードを出す
watch out		気を付けて！

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	have sent · have already created · has worked
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	might be asked · be done · can be done

注意したい発音

話し手はyou're, don't, I'mなど、短縮形や弱形を6回使っています。聞こえたとおりの短い形で発音しましょう。

「sh」と「zh」の音: essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, execution /ˌɛk.sɪˈkjuː.ʃən/, explanation /ˌɛkspləˈneɪʃən/
長い単語（アクセントの位置に注意）: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, essentially /ɪˈsɛnʃəli/, configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, collaborative /kəˈlæb.ə.ɹə.tɪv/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: collaborative /kəˈlæb.ə.ɹə.tɪv/, portal /ˈpɔːɹtl̩/
/v/ — /b/ にならないように、上の歯を下唇に当てる: collaborative /kəˈlæb.ə.ɹə.tɪv/, intensive /ɪnˈtɛnsɪv/, behave /bɪˈheɪv/, overhead /ˈəʊvəˌhɛd/, whichever /wɪt͡ʃˈɛvɚ/
/f/ — 「フ」ではなく、上の歯と下唇で出す: configuration /kənˌfɪɡ.əˈɹeɪ̯.ʃən/, transformation /ˌtɹæns.fɔɹˈmeɪ.ʃən/, fitness /ˈfɪt.nəs/, filter /ˈfɪltɚ/, graph /ɡɹæf/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。spark, worker, nodeなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: Databricks Interview Questions Part 1 - 動画で英語スピーキングを学ぶ

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シャドーイングとは？英語上達に効果的な理由